Air-to-Air Simulated Drone Dataset for AI-powered problems

This paper introduces the multi-view Air-to-Air Simulated Drone Dataset (A2A-SDD), a comprehensive simulated drone dataset captured using AirSim © . The dataset encompasses diverse scenarios where one or two drones are pursued by one to three monitoring drones. It includes five types of drones, such as DJI models and a generic quadrotor model, recorded in various weather conditions and environments. Both loaded and unloaded drones are represented, and the dataset provides extensive annotations, including object detection and XYZ co-ordinates. The dataset offers potential applications in training deep learning-based models for counter-UAV measures such as localization and payload detection in single- and multi-view cases. Furthermore, preliminary experiments demonstrate the promising performance of trained networks on practical data, affirming the dataset’s value in addressing real-world drone challenges using optical sensors. The synthetic dataset is publicly available on GitHub (https://github.com/CARG-uOttawa/Multiview-Air-to-Air-simulated-drone-dataset).

Cite as: H. Azad, V. Mehta, F. Dadboud, M. Bolic, I. Mantegh, Air-to-Air Simulated Drone Dataset for AI-powered problems, 2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC), pp. 1-7, 2023.
Download Paper | Download Slides